Monday, September 01, 2014
“The problem we’re trying to solve is that there are rich teams and there are poor teams. Then there’s fifty feet of crap, and then there’s us. It’s an unfair game. And now we’ve been gutted. We’re like organ donors for the rich. Boston’s taken our kidneys, Yankees have taken our heart. And you guys just sit around talking the same old “good body” nonsense like we’re selling jeans. Like we’re looking for Fabio. We’ve got to think differently. We are the last dog at the bowl. You see what happens to the runt of the litter? He dies.”
Luhnow’s appreciation of the predictive power of data grew out of his experience selling designer jeans. In the early 2000s, with a former president of Levi Strauss, he co-founded an online custom apparel company that made jeans for Lands’ End (LE) shoppers. “You’re taking self-reported inputs from a human being,” he says, “and then trying to figure out exactly what pair of pants to make them. Are they being honest with themselves? Is there vanity sizing involved? How do they perceive themselves relative to how they actually are?”
H/T to Jose Molina wants a nickname like “A-Rod”
Posted: September 01, 2014 at 12:40 AM | 12 comment(s)
Saturday, July 26, 2014
More Statcasting…and the death of the amateur sabermetrician?
Another incentive for the league—particularly in the face of rising salaries—is an improved understanding of player value, particularly on the defensive end, where teams have focused more attention in the past few years. Defensive shifts, once only employed by the wonkiest teams, have become a league-wide staple thanks to advances in batted-ball data. The problem is, shifts or not, evaluating glove work remains difficult, as most teams rely on some combination of scouting reports, play-by-play data and manual charting. Statcast offers something better: a way to focus on a player’s defensive attributes—his reaction time, range, route efficiency and so on—rather than his results, which are influenced by too many independent variables to list. This potential has excited many outside the industry (especially with the promise that the data will be made public) and even more within.
The only negative to come from all the new technology could be the death of the hobbyist. An important consideration, because some hobbyists have done research that changed how people inside and outside the industry approach the game. For example, Mike Fast was working as a physicist when he used PITCHf/x data to confirm the long-held suspicion that catchers influence how umpires call balls and strikes. Fast is now employed by the Houston Astros. Because the new data are so unwieldy, the barrier to entry so high, only select outsiders will possess the computing strength and wits to scale the wall. In time, someone might make a discovery that eluded the industry. Otherwise, the real advances—those that change how teams are built and how strategies are employed—will happen behind closed doors.
Thursday, July 24, 2014
Billy Beane should have never written this article.
Now that we have a sense of Beane’s performance and how much it would cost to replicate it, let’s turn back to the Boston Red Sox and their failure to sign him (or even to offer him anywhere near his worth)....
But some of that money was spent and some of those wins came before the Red Sox attempted to hire Beane. To be conservative, let’s just look at the period since Henry made Beane his offer: In the last 12 years, the Red Sox spent $1.714 billion on payroll, while the A’s spent $736 million. We can then break down what it could have looked like if Beane had worked for the Red Sox like so:
Let’s say it would have cost Boston the same $736 million that it cost Oakland to get the A’s performance with Beane.
At the hypothetical $25 million-per-year salary I suggested earlier, Beane would have cost the Red Sox another $300 million. (It’s possible that Beane would have wanted more, but it’s even more possible that they could have gotten him for less.)
The difference in performance between the A’s and the Red Sox over that period (where the Sox were as successful as at any point in the franchise’s history, and the A’s were supposedly stagnating after Beane’s early success) has been about 50 games for Boston. Since we don’t know exactly how good Beane would be at procuring additional wins above his Oakland performance, let’s assume that the Red Sox would have had to pay the typical amount teams have paid for wins in the period to make up the difference. According to the year-by-year price of wins from my calculations above, those 50 wins (taking when they happened into account) would have a market value of about $370 million (though this might have been lower with Beane in charge).
If we combine these — the price of the A’s performance ($736 million) plus Super-Expensive-Billy-Beane’s salary ($300 million) plus the additional 50 Red Sox wins at high market estimates ($370 million) – merely duplicating their previous level of success still would have saved the Red Sox more than $300 million relative to what they actually spent, and that’s with reasonably conservative assumptions. That’s money they could have pocketed, or spent making themselves even better.
Monday, July 14, 2014
If plate patience was so important, why don’t they put it on the scoreboard?
But the problem with this narrative goes deeper than that. In truth, there is little evidence of any ultimate connection between team plate patience and production, on either side of the ledger. So far this season, the Red Sox (4.02 P/PA), Mets (3.87 P/PA), and Phillies (3.87 P/PA) all have significantly above-average plate patience, and significantly below-average offensive production. The Blue Jays, Athletics, and Indians have significantly above-average plate patience with above-average offensive production….
We can find some effect of plate patience if we step back to hits, strikeouts, and walks. Over this same time period, there is a significant (p<.05) but weak (r=.21) positive connection between team P/PA and hits, a highly significant (p<.001) and moderate (r=.4) positive connection between team P/PA and strikeouts, and a highly significant (p<.001) and rather strong (r=.56) positive connection between team P/PA and walks.
The connection between plate patience and walks/strikeouts at least makes sense: the more pitches you take, the more likely a hitter will get to strike three or ball four. But when you re-run any of the bottom-line metrics we care about, like wRC+ or Runs Scored—even controlling for walks and strikeouts—there still is no meaningful connection between team plate patience and team offensive success, at least over recent seasons.
for his generous support.
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